ANCOVA in flow (debat) (flow
(debat))
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
flow (debat) (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in flow (debat) (measured using pre- and post-tests).
Setting Initial Variables
dv = "flow.debat"
dv.pos = "fss.debate"
dv.pre = "dfs.debate"
fatores2 <- c("genero","zona.participante","zona.escola")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#008000")
color[["genero"]] = c("#FF007F","#4D4DFF")
color[["zona.escola"]] = c("#AA00FF","#00CCCC")
color[["zona.participante"]] = c("#AA00FF","#00CCCC")
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["genero"]] = c("F","M")
level[["zona.escola"]] = c("Rural","Urbana")
level[["zona.participante"]] = c("Rural","Urbana")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:genero"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:zona.escola"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:zona.participante"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
for (coln in c(
"palavras.lidas","score.compreensao","tri.compreensao",
"score.vocab","tri.vocab",
"score.vocab.ensinado","tri.vocab.ensinado","score.vocab.nao.ensinado","tri.vocab.nao.ensinado",
"score.CLPP","tri.CLPP","score.CR","tri.CR",
"score.CI","tri.CI","score.TV","tri.TV","score.TF","tri.TF","score.TO","tri.TO")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "flow.wg.wo.st")
dat <- gdat
dat$grupo <- factor(dat[["grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
dfs.debate |
122 |
3.462 |
3.444 |
2.000 |
4.556 |
0.562 |
0.051 |
0.101 |
0.778 |
YES |
-0.318 |
-0.316 |
| Experimental |
|
|
|
dfs.debate |
115 |
3.448 |
3.556 |
2.222 |
4.667 |
0.535 |
0.050 |
0.099 |
0.722 |
YES |
-0.063 |
-0.553 |
|
|
|
|
dfs.debate |
237 |
3.455 |
3.444 |
2.000 |
4.667 |
0.548 |
0.036 |
0.070 |
0.667 |
YES |
-0.203 |
-0.398 |
| Controle |
|
|
|
fss.debate |
122 |
3.426 |
3.444 |
2.111 |
4.333 |
0.505 |
0.046 |
0.091 |
0.667 |
YES |
-0.155 |
-0.346 |
| Experimental |
|
|
|
fss.debate |
115 |
3.551 |
3.556 |
2.111 |
5.000 |
0.564 |
0.053 |
0.104 |
0.667 |
YES |
0.076 |
0.066 |
|
|
|
|
fss.debate |
237 |
3.487 |
3.444 |
2.111 |
5.000 |
0.537 |
0.035 |
0.069 |
0.667 |
YES |
0.014 |
0.009 |
| Controle |
F |
|
|
dfs.debate |
60 |
3.481 |
3.556 |
2.333 |
4.556 |
0.542 |
0.070 |
0.140 |
0.795 |
YES |
-0.140 |
-0.542 |
| Controle |
M |
|
|
dfs.debate |
62 |
3.444 |
3.444 |
2.000 |
4.556 |
0.586 |
0.074 |
0.149 |
0.750 |
YES |
-0.434 |
-0.301 |
| Experimental |
F |
|
|
dfs.debate |
52 |
3.560 |
3.667 |
2.222 |
4.667 |
0.520 |
0.072 |
0.145 |
0.472 |
NO |
-0.528 |
0.315 |
| Experimental |
M |
|
|
dfs.debate |
63 |
3.356 |
3.250 |
2.375 |
4.556 |
0.535 |
0.067 |
0.135 |
0.889 |
YES |
0.312 |
-0.779 |
| Controle |
F |
|
|
fss.debate |
60 |
3.533 |
3.556 |
2.444 |
4.333 |
0.492 |
0.063 |
0.127 |
0.580 |
YES |
-0.055 |
-0.748 |
| Controle |
M |
|
|
fss.debate |
62 |
3.322 |
3.333 |
2.111 |
4.222 |
0.501 |
0.064 |
0.127 |
0.667 |
YES |
-0.241 |
-0.269 |
| Experimental |
F |
|
|
fss.debate |
52 |
3.683 |
3.667 |
2.111 |
4.750 |
0.529 |
0.073 |
0.147 |
0.694 |
YES |
-0.064 |
0.215 |
| Experimental |
M |
|
|
fss.debate |
63 |
3.442 |
3.444 |
2.111 |
5.000 |
0.573 |
0.072 |
0.144 |
0.667 |
YES |
0.255 |
0.127 |
| Controle |
|
Rural |
|
dfs.debate |
43 |
3.532 |
3.556 |
2.444 |
4.556 |
0.526 |
0.080 |
0.162 |
0.889 |
YES |
-0.064 |
-0.900 |
| Controle |
|
Urbana |
|
dfs.debate |
51 |
3.365 |
3.444 |
2.000 |
4.500 |
0.628 |
0.088 |
0.177 |
0.833 |
YES |
-0.425 |
-0.676 |
| Controle |
|
|
|
dfs.debate |
28 |
3.531 |
3.500 |
2.778 |
4.556 |
0.476 |
0.090 |
0.185 |
0.556 |
YES |
0.302 |
-0.524 |
| Experimental |
|
Rural |
|
dfs.debate |
47 |
3.441 |
3.444 |
2.556 |
4.556 |
0.541 |
0.079 |
0.159 |
0.722 |
YES |
0.343 |
-0.664 |
| Experimental |
|
Urbana |
|
dfs.debate |
43 |
3.393 |
3.444 |
2.222 |
4.667 |
0.578 |
0.088 |
0.178 |
0.833 |
YES |
-0.098 |
-0.757 |
| Experimental |
|
|
|
dfs.debate |
25 |
3.557 |
3.667 |
2.500 |
4.222 |
0.443 |
0.089 |
0.183 |
0.500 |
NO |
-0.820 |
0.071 |
| Controle |
|
Rural |
|
fss.debate |
43 |
3.364 |
3.444 |
2.111 |
4.333 |
0.523 |
0.080 |
0.161 |
0.667 |
YES |
0.091 |
-0.556 |
| Controle |
|
Urbana |
|
fss.debate |
51 |
3.443 |
3.375 |
2.111 |
4.333 |
0.525 |
0.073 |
0.148 |
0.604 |
YES |
-0.271 |
-0.168 |
| Controle |
|
|
|
fss.debate |
28 |
3.488 |
3.646 |
2.556 |
4.222 |
0.446 |
0.084 |
0.173 |
0.583 |
YES |
-0.245 |
-0.889 |
| Experimental |
|
Rural |
|
fss.debate |
47 |
3.456 |
3.444 |
2.111 |
4.750 |
0.572 |
0.083 |
0.168 |
0.715 |
YES |
0.096 |
-0.003 |
| Experimental |
|
Urbana |
|
fss.debate |
43 |
3.588 |
3.556 |
2.625 |
4.778 |
0.504 |
0.077 |
0.155 |
0.611 |
YES |
0.362 |
-0.241 |
| Experimental |
|
|
|
fss.debate |
25 |
3.667 |
3.667 |
2.111 |
5.000 |
0.635 |
0.127 |
0.262 |
0.778 |
YES |
-0.238 |
-0.072 |
| Controle |
|
|
Rural |
dfs.debate |
41 |
3.518 |
3.556 |
2.111 |
4.556 |
0.540 |
0.084 |
0.171 |
0.667 |
YES |
-0.424 |
0.099 |
| Controle |
|
|
Urbana |
dfs.debate |
81 |
3.434 |
3.444 |
2.000 |
4.556 |
0.575 |
0.064 |
0.127 |
0.889 |
YES |
-0.253 |
-0.534 |
| Experimental |
|
|
Rural |
dfs.debate |
35 |
3.537 |
3.667 |
2.556 |
4.556 |
0.533 |
0.090 |
0.183 |
0.694 |
YES |
-0.031 |
-0.731 |
| Experimental |
|
|
Urbana |
dfs.debate |
80 |
3.410 |
3.444 |
2.222 |
4.667 |
0.535 |
0.060 |
0.119 |
0.778 |
YES |
-0.074 |
-0.566 |
| Controle |
|
|
Rural |
fss.debate |
41 |
3.422 |
3.444 |
2.556 |
4.333 |
0.418 |
0.065 |
0.132 |
0.556 |
YES |
0.140 |
-0.509 |
| Controle |
|
|
Urbana |
fss.debate |
81 |
3.427 |
3.444 |
2.111 |
4.333 |
0.547 |
0.061 |
0.121 |
0.667 |
YES |
-0.220 |
-0.509 |
| Experimental |
|
|
Rural |
fss.debate |
35 |
3.552 |
3.444 |
2.111 |
4.750 |
0.623 |
0.105 |
0.214 |
0.889 |
YES |
-0.032 |
-0.503 |
| Experimental |
|
|
Urbana |
fss.debate |
80 |
3.551 |
3.556 |
2.111 |
5.000 |
0.540 |
0.060 |
0.120 |
0.580 |
YES |
0.145 |
0.301 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "fss.debate", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.debat"]] <- c(pdat[["dfs.debate"]], pdat[["fss.debate"]])
aov = anova_test(pdat, fss.debate ~ dfs.debate + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, fss.debate ~ grupo, covariate = dfs.debate,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "fss.debate", "grupo", covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.debate","se.dfs.debate","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(fss.debate ~ dfs.debate + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.debat"]] <- c(wdat[["dfs.debate"]], wdat[["fss.debate"]])
ldat[["grupo"]] = wdat
(non.normal)
## NULL
aov = anova_test(wdat, fss.debate ~ dfs.debate + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dfs.debate 1 234 37.158 4.46e-09 * 0.137
## 2 grupo 1 234 4.071 4.50e-02 * 0.017
| dfs.debate |
1 |
234 |
37.158 |
0.000 |
* |
0.137 |
| grupo |
1 |
234 |
4.071 |
0.045 |
* |
0.017 |
pwc <- emmeans_test(wdat, fss.debate ~ grupo, covariate = dfs.debate,
p.adjust.method = "bonferroni")
| dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
234 |
-2.018 |
0.045 |
0.045 |
* |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
flow.debat |
pre |
pos |
470 |
0.524 |
0.60 |
0.60 |
ns |
| Experimental |
time |
flow.debat |
pre |
pos |
470 |
-1.441 |
0.15 |
0.15 |
ns |
ds <- get.descriptives(wdat, "fss.debate", "grupo", covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.debate","se.dfs.debate","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
122 |
3.462 |
0.051 |
3.426 |
0.046 |
3.423 |
0.045 |
3.335 |
3.512 |
| Experimental |
115 |
3.448 |
0.050 |
3.551 |
0.053 |
3.554 |
0.046 |
3.462 |
3.645 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "fss.debate", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] + ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "fss.debate", "grupo", aov, pwc, covar = "dfs.debate",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("flow (debat)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "flow.debat", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(fss.debate ~ dfs.debate + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.997 0.972
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 235 0.375 0.541
ANCOVA and
Pairwise for two factors grupo:genero
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["genero"]]),],
"fss.debate", c("grupo","genero"))
pdat = pdat[pdat[["genero"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["genero"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["genero"]] = factor(
pdat[["genero"]],
level[["genero"]][level[["genero"]] %in% unique(pdat[["genero"]])])
pdat.long <- rbind(pdat[,c("id","grupo","genero")], pdat[,c("id","grupo","genero")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.debat"]] <- c(pdat[["dfs.debate"]], pdat[["fss.debate"]])
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(pdat, fss.debate ~ dfs.debate + grupo*genero)
laov[["grupo:genero"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(pdat, grupo), fss.debate ~ genero,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, genero), fss.debate ~ grupo,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","genero")),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.debate", c("grupo","genero"), covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.dfs.debate","se.dfs.debate","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["genero"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.debate ~ dfs.debate + grupo*genero, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","genero")], wdat[,c("id","grupo","genero")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.debat"]] <- c(wdat[["dfs.debate"]], wdat[["fss.debate"]])
ldat[["grupo:genero"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(wdat, fss.debate ~ dfs.debate + grupo*genero)
laov[["grupo:genero"]] <- merge(get_anova_table(aov), laov[["grupo:genero"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.debate |
1 |
232 |
34.002 |
0.000 |
* |
0.128 |
| grupo |
1 |
232 |
4.640 |
0.032 |
* |
0.020 |
| genero |
1 |
232 |
8.262 |
0.004 |
* |
0.034 |
| grupo:genero |
1 |
232 |
0.042 |
0.837 |
|
0.000 |
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(wdat, grupo), fss.debate ~ genero,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, genero), fss.debate ~ grupo,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
|
F |
dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
232 |
-1.327 |
0.186 |
0.186 |
ns |
|
M |
dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
232 |
-1.705 |
0.090 |
0.090 |
ns |
| Controle |
|
dfs.debate*genero |
fss.debate |
F |
M |
232 |
2.220 |
0.027 |
0.027 |
* |
| Experimental |
|
dfs.debate*genero |
fss.debate |
F |
M |
232 |
1.844 |
0.066 |
0.066 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","genero")),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:genero"]],
by=c("grupo","genero","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
flow.debat |
pre |
pos |
466 |
-0.525 |
0.600 |
0.600 |
ns |
| Controle |
M |
time |
flow.debat |
pre |
pos |
466 |
1.260 |
0.208 |
0.208 |
ns |
| Experimental |
F |
time |
flow.debat |
pre |
pos |
466 |
-1.174 |
0.241 |
0.241 |
ns |
| Experimental |
M |
time |
flow.debat |
pre |
pos |
466 |
-0.903 |
0.367 |
0.367 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.debate", c("grupo","genero"), covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.dfs.debate","se.dfs.debate",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- merge(ds, lemms[["grupo:genero"]],
by=c("grupo","genero"), suffixes = c("","'"))
}
| Controle |
F |
60 |
3.481 |
0.070 |
3.533 |
0.063 |
3.524 |
0.063 |
3.399 |
3.649 |
| Controle |
M |
62 |
3.444 |
0.074 |
3.322 |
0.064 |
3.326 |
0.062 |
3.203 |
3.449 |
| Experimental |
F |
52 |
3.560 |
0.072 |
3.683 |
0.073 |
3.647 |
0.068 |
3.513 |
3.782 |
| Experimental |
M |
63 |
3.356 |
0.067 |
3.442 |
0.072 |
3.476 |
0.062 |
3.354 |
3.599 |
Plots for ancova
if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "genero", aov, ylab = "flow (debat)",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "genero", "grupo", aov, ylab = "flow (debat)",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.debate", c("grupo","genero"), aov, pwcs, covar = "dfs.debate",
theme = "classic", color = color[["grupo:genero"]],
subtitle = which(aov$Effect == "grupo:genero"))
}
if (length(unique(pdat[["genero"]])) >= 2) {
plots[["grupo:genero"]] + ggplot2::ylab("flow (debat)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.debat", c("grupo","genero"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["genero"]])) >= 2)
plots[["grupo:genero"]] + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
facet.by = c("grupo","genero"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "grupo", facet.by = "genero", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "genero", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = genero)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["genero"]])) >= 2)
res <- augment(lm(fss.debate ~ dfs.debate + grupo*genero, data = wdat))
if (length(unique(pdat[["genero"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.996 0.815
if (length(unique(pdat[["genero"]])) >= 2)
levene_test(res, .resid ~ grupo*genero)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 233 1.15 0.329
ANCOVA
and Pairwise for two factors
grupo:zona.participante
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.participante"]]),],
"fss.debate", c("grupo","zona.participante"))
pdat = pdat[pdat[["zona.participante"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.participante"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.participante"]] = factor(
pdat[["zona.participante"]],
level[["zona.participante"]][level[["zona.participante"]] %in% unique(pdat[["zona.participante"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.participante")], pdat[,c("id","grupo","zona.participante")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.debat"]] <- c(pdat[["dfs.debate"]], pdat[["fss.debate"]])
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(pdat, fss.debate ~ dfs.debate + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(pdat, grupo), fss.debate ~ zona.participante,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.participante), fss.debate ~ grupo,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.participante")),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.debate", c("grupo","zona.participante"), covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.dfs.debate","se.dfs.debate","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.participante"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.debate ~ dfs.debate + grupo*zona.participante, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.participante")], wdat[,c("id","grupo","zona.participante")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.debat"]] <- c(wdat[["dfs.debate"]], wdat[["fss.debate"]])
ldat[["grupo:zona.participante"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(wdat, fss.debate ~ dfs.debate + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- merge(get_anova_table(aov), laov[["grupo:zona.participante"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.debate |
1 |
179 |
25.189 |
0.000 |
* |
0.123 |
| grupo |
1 |
179 |
3.038 |
0.083 |
|
0.017 |
| zona.participante |
1 |
179 |
3.580 |
0.060 |
|
0.020 |
| grupo:zona.participante |
1 |
179 |
0.008 |
0.927 |
|
0.000 |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(wdat, grupo), fss.debate ~ zona.participante,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.participante), fss.debate ~ grupo,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
|
Rural |
dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
179 |
-1.154 |
0.250 |
0.250 |
ns |
|
Urbana |
dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
179 |
-1.308 |
0.192 |
0.192 |
ns |
| Controle |
|
dfs.debate*zona.participante |
fss.debate |
Rural |
Urbana |
179 |
-1.285 |
0.200 |
0.200 |
ns |
| Experimental |
|
dfs.debate*zona.participante |
fss.debate |
Rural |
Urbana |
179 |
-1.395 |
0.165 |
0.165 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.participante")),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.participante"]],
by=c("grupo","zona.participante","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
flow.debat |
pre |
pos |
360 |
1.409 |
0.160 |
0.160 |
ns |
| Controle |
Urbana |
time |
flow.debat |
pre |
pos |
360 |
-0.715 |
0.475 |
0.475 |
ns |
| Experimental |
Rural |
time |
flow.debat |
pre |
pos |
360 |
-0.135 |
0.893 |
0.893 |
ns |
| Experimental |
Urbana |
time |
flow.debat |
pre |
pos |
360 |
-1.635 |
0.103 |
0.103 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.debate", c("grupo","zona.participante"), covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.dfs.debate","se.dfs.debate",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- merge(ds, lemms[["grupo:zona.participante"]],
by=c("grupo","zona.participante"), suffixes = c("","'"))
}
| Controle |
Rural |
43 |
3.532 |
0.080 |
3.364 |
0.080 |
3.331 |
0.076 |
3.180 |
3.482 |
| Controle |
Urbana |
51 |
3.365 |
0.088 |
3.443 |
0.073 |
3.465 |
0.070 |
3.326 |
3.603 |
| Experimental |
Rural |
47 |
3.441 |
0.079 |
3.456 |
0.083 |
3.453 |
0.073 |
3.309 |
3.597 |
| Experimental |
Urbana |
43 |
3.393 |
0.088 |
3.588 |
0.077 |
3.600 |
0.076 |
3.449 |
3.750 |
Plots for ancova
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.participante", aov, ylab = "flow (debat)",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.participante", "grupo", aov, ylab = "flow (debat)",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.debate", c("grupo","zona.participante"), aov, pwcs, covar = "dfs.debate",
theme = "classic", color = color[["grupo:zona.participante"]],
subtitle = which(aov$Effect == "grupo:zona.participante"))
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots[["grupo:zona.participante"]] + ggplot2::ylab("flow (debat)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.debat", c("grupo","zona.participante"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.participante"]])) >= 2)
plots[["grupo:zona.participante"]] + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
facet.by = c("grupo","zona.participante"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "grupo", facet.by = "zona.participante", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "zona.participante", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.participante)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.participante"]])) >= 2)
res <- augment(lm(fss.debate ~ dfs.debate + grupo*zona.participante, data = wdat))
if (length(unique(pdat[["zona.participante"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.995 0.824
if (length(unique(pdat[["zona.participante"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.participante)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 180 0.816 0.487
ANCOVA and
Pairwise for two factors grupo:zona.escola
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.escola"]]),],
"fss.debate", c("grupo","zona.escola"))
pdat = pdat[pdat[["zona.escola"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.escola"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.escola"]] = factor(
pdat[["zona.escola"]],
level[["zona.escola"]][level[["zona.escola"]] %in% unique(pdat[["zona.escola"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.escola")], pdat[,c("id","grupo","zona.escola")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.debat"]] <- c(pdat[["dfs.debate"]], pdat[["fss.debate"]])
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(pdat, fss.debate ~ dfs.debate + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(pdat, grupo), fss.debate ~ zona.escola,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.escola), fss.debate ~ grupo,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.escola")),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.debate", c("grupo","zona.escola"), covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.dfs.debate","se.dfs.debate","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.escola"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.debate ~ dfs.debate + grupo*zona.escola, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.escola")], wdat[,c("id","grupo","zona.escola")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.debat"]] <- c(wdat[["dfs.debate"]], wdat[["fss.debate"]])
ldat[["grupo:zona.escola"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(wdat, fss.debate ~ dfs.debate + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- merge(get_anova_table(aov), laov[["grupo:zona.escola"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.debate |
1 |
232 |
37.222 |
0.000 |
* |
0.138 |
| grupo |
1 |
232 |
3.963 |
0.048 |
* |
0.017 |
| zona.escola |
1 |
232 |
0.328 |
0.567 |
|
0.001 |
| grupo:zona.escola |
1 |
232 |
0.005 |
0.946 |
|
0.000 |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(wdat, grupo), fss.debate ~ zona.escola,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.escola), fss.debate ~ grupo,
covariate = dfs.debate, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
|
Rural |
dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
232 |
-1.069 |
0.286 |
0.286 |
ns |
|
Urbana |
dfs.debate*grupo |
fss.debate |
Controle |
Experimental |
232 |
-1.681 |
0.094 |
0.094 |
ns |
| Controle |
|
dfs.debate*zona.escola |
fss.debate |
Rural |
Urbana |
232 |
-0.370 |
0.711 |
0.711 |
ns |
| Experimental |
|
dfs.debate*zona.escola |
fss.debate |
Rural |
Urbana |
232 |
-0.443 |
0.658 |
0.658 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.escola")),
flow.debat ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.escola"]],
by=c("grupo","zona.escola","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
flow.debat |
pre |
pos |
466 |
0.796 |
0.426 |
0.426 |
ns |
| Controle |
Urbana |
time |
flow.debat |
pre |
pos |
466 |
0.075 |
0.940 |
0.940 |
ns |
| Experimental |
Rural |
time |
flow.debat |
pre |
pos |
466 |
-0.119 |
0.905 |
0.905 |
ns |
| Experimental |
Urbana |
time |
flow.debat |
pre |
pos |
466 |
-1.645 |
0.101 |
0.101 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.debate", c("grupo","zona.escola"), covar = "dfs.debate")
ds <- merge(ds[ds$variable != "dfs.debate",],
ds[ds$variable == "dfs.debate", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".dfs.debate"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.dfs.debate","se.dfs.debate",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- merge(ds, lemms[["grupo:zona.escola"]],
by=c("grupo","zona.escola"), suffixes = c("","'"))
}
| Controle |
Rural |
41 |
3.518 |
0.084 |
3.422 |
0.065 |
3.400 |
0.078 |
3.246 |
3.554 |
| Controle |
Urbana |
81 |
3.434 |
0.064 |
3.427 |
0.061 |
3.435 |
0.056 |
3.326 |
3.545 |
| Experimental |
Rural |
35 |
3.537 |
0.090 |
3.552 |
0.105 |
3.523 |
0.085 |
3.356 |
3.689 |
| Experimental |
Urbana |
80 |
3.410 |
0.060 |
3.551 |
0.060 |
3.568 |
0.056 |
3.457 |
3.678 |
Plots for ancova
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.escola", aov, ylab = "flow (debat)",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.escola", "grupo", aov, ylab = "flow (debat)",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.debate", c("grupo","zona.escola"), aov, pwcs, covar = "dfs.debate",
theme = "classic", color = color[["grupo:zona.escola"]],
subtitle = which(aov$Effect == "grupo:zona.escola"))
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots[["grupo:zona.escola"]] + ggplot2::ylab("flow (debat)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.debat", c("grupo","zona.escola"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.escola"]])) >= 2)
plots[["grupo:zona.escola"]] + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
facet.by = c("grupo","zona.escola"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "grupo", facet.by = "zona.escola", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "dfs.debate", y = "fss.debate", size = 0.5,
color = "zona.escola", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.escola)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("flow (debat)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.escola"]])) >= 2)
res <- augment(lm(fss.debate ~ dfs.debate + grupo*zona.escola, data = wdat))
if (length(unique(pdat[["zona.escola"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.998 0.977
if (length(unique(pdat[["zona.escola"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.escola)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 233 0.772 0.511
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
dfs.debate |
122 |
3.462 |
3.444 |
2.000 |
4.556 |
0.562 |
0.051 |
0.101 |
0.778 |
YES |
-0.318 |
-0.316 |
| Experimental |
|
|
|
dfs.debate |
115 |
3.448 |
3.556 |
2.222 |
4.667 |
0.535 |
0.050 |
0.099 |
0.722 |
YES |
-0.063 |
-0.553 |
|
|
|
|
dfs.debate |
237 |
3.455 |
3.444 |
2.000 |
4.667 |
0.548 |
0.036 |
0.070 |
0.667 |
YES |
-0.203 |
-0.398 |
| Controle |
|
|
|
fss.debate |
122 |
3.426 |
3.444 |
2.111 |
4.333 |
0.505 |
0.046 |
0.091 |
0.667 |
YES |
-0.155 |
-0.346 |
| Experimental |
|
|
|
fss.debate |
115 |
3.551 |
3.556 |
2.111 |
5.000 |
0.564 |
0.053 |
0.104 |
0.667 |
YES |
0.076 |
0.066 |
|
|
|
|
fss.debate |
237 |
3.487 |
3.444 |
2.111 |
5.000 |
0.537 |
0.035 |
0.069 |
0.667 |
YES |
0.014 |
0.009 |
| Controle |
F |
|
|
dfs.debate |
60 |
3.481 |
3.556 |
2.333 |
4.556 |
0.542 |
0.070 |
0.140 |
0.795 |
YES |
-0.140 |
-0.542 |
| Controle |
M |
|
|
dfs.debate |
62 |
3.444 |
3.444 |
2.000 |
4.556 |
0.586 |
0.074 |
0.149 |
0.750 |
YES |
-0.434 |
-0.301 |
| Experimental |
F |
|
|
dfs.debate |
52 |
3.560 |
3.667 |
2.222 |
4.667 |
0.520 |
0.072 |
0.145 |
0.472 |
NO |
-0.528 |
0.315 |
| Experimental |
M |
|
|
dfs.debate |
63 |
3.356 |
3.250 |
2.375 |
4.556 |
0.535 |
0.067 |
0.135 |
0.889 |
YES |
0.312 |
-0.779 |
| Controle |
F |
|
|
fss.debate |
60 |
3.533 |
3.556 |
2.444 |
4.333 |
0.492 |
0.063 |
0.127 |
0.580 |
YES |
-0.055 |
-0.748 |
| Controle |
M |
|
|
fss.debate |
62 |
3.322 |
3.333 |
2.111 |
4.222 |
0.501 |
0.064 |
0.127 |
0.667 |
YES |
-0.241 |
-0.269 |
| Experimental |
F |
|
|
fss.debate |
52 |
3.683 |
3.667 |
2.111 |
4.750 |
0.529 |
0.073 |
0.147 |
0.694 |
YES |
-0.064 |
0.215 |
| Experimental |
M |
|
|
fss.debate |
63 |
3.442 |
3.444 |
2.111 |
5.000 |
0.573 |
0.072 |
0.144 |
0.667 |
YES |
0.255 |
0.127 |
| Controle |
|
Rural |
|
dfs.debate |
43 |
3.532 |
3.556 |
2.444 |
4.556 |
0.526 |
0.080 |
0.162 |
0.889 |
YES |
-0.064 |
-0.900 |
| Controle |
|
Urbana |
|
dfs.debate |
51 |
3.365 |
3.444 |
2.000 |
4.500 |
0.628 |
0.088 |
0.177 |
0.833 |
YES |
-0.425 |
-0.676 |
| Experimental |
|
Rural |
|
dfs.debate |
47 |
3.441 |
3.444 |
2.556 |
4.556 |
0.541 |
0.079 |
0.159 |
0.722 |
YES |
0.343 |
-0.664 |
| Experimental |
|
Urbana |
|
dfs.debate |
43 |
3.393 |
3.444 |
2.222 |
4.667 |
0.578 |
0.088 |
0.178 |
0.833 |
YES |
-0.098 |
-0.757 |
| Controle |
|
Rural |
|
fss.debate |
43 |
3.364 |
3.444 |
2.111 |
4.333 |
0.523 |
0.080 |
0.161 |
0.667 |
YES |
0.091 |
-0.556 |
| Controle |
|
Urbana |
|
fss.debate |
51 |
3.443 |
3.375 |
2.111 |
4.333 |
0.525 |
0.073 |
0.148 |
0.604 |
YES |
-0.271 |
-0.168 |
| Experimental |
|
Rural |
|
fss.debate |
47 |
3.456 |
3.444 |
2.111 |
4.750 |
0.572 |
0.083 |
0.168 |
0.715 |
YES |
0.096 |
-0.003 |
| Experimental |
|
Urbana |
|
fss.debate |
43 |
3.588 |
3.556 |
2.625 |
4.778 |
0.504 |
0.077 |
0.155 |
0.611 |
YES |
0.362 |
-0.241 |
| Controle |
|
|
Rural |
dfs.debate |
41 |
3.518 |
3.556 |
2.111 |
4.556 |
0.540 |
0.084 |
0.171 |
0.667 |
YES |
-0.424 |
0.099 |
| Controle |
|
|
Urbana |
dfs.debate |
81 |
3.434 |
3.444 |
2.000 |
4.556 |
0.575 |
0.064 |
0.127 |
0.889 |
YES |
-0.253 |
-0.534 |
| Experimental |
|
|
Rural |
dfs.debate |
35 |
3.537 |
3.667 |
2.556 |
4.556 |
0.533 |
0.090 |
0.183 |
0.694 |
YES |
-0.031 |
-0.731 |
| Experimental |
|
|
Urbana |
dfs.debate |
80 |
3.410 |
3.444 |
2.222 |
4.667 |
0.535 |
0.060 |
0.119 |
0.778 |
YES |
-0.074 |
-0.566 |
| Controle |
|
|
Rural |
fss.debate |
41 |
3.422 |
3.444 |
2.556 |
4.333 |
0.418 |
0.065 |
0.132 |
0.556 |
YES |
0.140 |
-0.509 |
| Controle |
|
|
Urbana |
fss.debate |
81 |
3.427 |
3.444 |
2.111 |
4.333 |
0.547 |
0.061 |
0.121 |
0.667 |
YES |
-0.220 |
-0.509 |
| Experimental |
|
|
Rural |
fss.debate |
35 |
3.552 |
3.444 |
2.111 |
4.750 |
0.623 |
0.105 |
0.214 |
0.889 |
YES |
-0.032 |
-0.503 |
| Experimental |
|
|
Urbana |
fss.debate |
80 |
3.551 |
3.556 |
2.111 |
5.000 |
0.540 |
0.060 |
0.120 |
0.580 |
YES |
0.145 |
0.301 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
dfs.debate |
1 |
234 |
37.158 |
0.000 |
* |
0.137 |
1 |
234 |
37.158 |
0.000 |
* |
0.137 |
| 2 |
grupo |
1 |
234 |
4.071 |
0.045 |
* |
0.017 |
1 |
234 |
4.071 |
0.045 |
* |
0.017 |
| 4 |
genero |
1 |
232 |
8.262 |
0.004 |
* |
0.034 |
1 |
232 |
8.262 |
0.004 |
* |
0.034 |
| 6 |
grupo:genero |
1 |
232 |
0.042 |
0.837 |
|
0.000 |
1 |
232 |
0.042 |
0.837 |
|
0.000 |
| 9 |
grupo:zona.participante |
1 |
179 |
0.008 |
0.927 |
|
0.000 |
1 |
179 |
0.008 |
0.927 |
|
0.000 |
| 10 |
zona.participante |
1 |
179 |
3.580 |
0.060 |
|
0.020 |
1 |
179 |
3.580 |
0.060 |
|
0.020 |
| 13 |
grupo:zona.escola |
1 |
232 |
0.005 |
0.946 |
|
0.000 |
1 |
232 |
0.005 |
0.946 |
|
0.000 |
| 14 |
zona.escola |
1 |
232 |
0.328 |
0.567 |
|
0.001 |
1 |
232 |
0.328 |
0.567 |
|
0.001 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
pre |
pos |
470 |
0.524 |
0.600 |
0.600 |
ns |
470 |
0.524 |
0.600 |
0.600 |
ns |
| Experimental |
|
|
|
pre |
pos |
470 |
-1.441 |
0.150 |
0.150 |
ns |
470 |
-1.441 |
0.150 |
0.150 |
ns |
|
|
|
|
Controle |
Experimental |
234 |
-2.018 |
0.045 |
0.045 |
* |
234 |
-2.018 |
0.045 |
0.045 |
* |
| Controle |
F |
|
|
pre |
pos |
466 |
-0.525 |
0.600 |
0.600 |
ns |
466 |
-0.525 |
0.600 |
0.600 |
ns |
| Controle |
M |
|
|
pre |
pos |
466 |
1.260 |
0.208 |
0.208 |
ns |
466 |
1.260 |
0.208 |
0.208 |
ns |
| Controle |
|
|
|
F |
M |
232 |
2.220 |
0.027 |
0.027 |
* |
232 |
2.220 |
0.027 |
0.027 |
* |
| Experimental |
F |
|
|
pre |
pos |
466 |
-1.174 |
0.241 |
0.241 |
ns |
466 |
-1.174 |
0.241 |
0.241 |
ns |
| Experimental |
M |
|
|
pre |
pos |
466 |
-0.903 |
0.367 |
0.367 |
ns |
466 |
-0.903 |
0.367 |
0.367 |
ns |
| Experimental |
|
|
|
F |
M |
232 |
1.844 |
0.066 |
0.066 |
ns |
232 |
1.844 |
0.066 |
0.066 |
ns |
|
F |
|
|
Controle |
Experimental |
232 |
-1.327 |
0.186 |
0.186 |
ns |
232 |
-1.327 |
0.186 |
0.186 |
ns |
|
M |
|
|
Controle |
Experimental |
232 |
-1.705 |
0.090 |
0.090 |
ns |
232 |
-1.705 |
0.090 |
0.090 |
ns |
| Controle |
|
|
|
Rural |
Urbana |
179 |
-1.285 |
0.200 |
0.200 |
ns |
179 |
-1.285 |
0.200 |
0.200 |
ns |
| Controle |
|
Rural |
|
pre |
pos |
360 |
1.409 |
0.160 |
0.160 |
ns |
360 |
1.409 |
0.160 |
0.160 |
ns |
| Controle |
|
Urbana |
|
pre |
pos |
360 |
-0.715 |
0.475 |
0.475 |
ns |
360 |
-0.715 |
0.475 |
0.475 |
ns |
| Experimental |
|
|
|
Rural |
Urbana |
179 |
-1.395 |
0.165 |
0.165 |
ns |
179 |
-1.395 |
0.165 |
0.165 |
ns |
| Experimental |
|
Rural |
|
pre |
pos |
360 |
-0.135 |
0.893 |
0.893 |
ns |
360 |
-0.135 |
0.893 |
0.893 |
ns |
| Experimental |
|
Urbana |
|
pre |
pos |
360 |
-1.635 |
0.103 |
0.103 |
ns |
360 |
-1.635 |
0.103 |
0.103 |
ns |
|
|
Rural |
|
Controle |
Experimental |
179 |
-1.154 |
0.250 |
0.250 |
ns |
179 |
-1.154 |
0.250 |
0.250 |
ns |
|
|
Urbana |
|
Controle |
Experimental |
179 |
-1.308 |
0.192 |
0.192 |
ns |
179 |
-1.308 |
0.192 |
0.192 |
ns |
| Controle |
|
|
|
Rural |
Urbana |
232 |
-0.370 |
0.711 |
0.711 |
ns |
232 |
-0.370 |
0.711 |
0.711 |
ns |
| Controle |
|
|
Rural |
pre |
pos |
466 |
0.796 |
0.426 |
0.426 |
ns |
466 |
0.796 |
0.426 |
0.426 |
ns |
| Controle |
|
|
Urbana |
pre |
pos |
466 |
0.075 |
0.940 |
0.940 |
ns |
466 |
0.075 |
0.940 |
0.940 |
ns |
| Experimental |
|
|
|
Rural |
Urbana |
232 |
-0.443 |
0.658 |
0.658 |
ns |
232 |
-0.443 |
0.658 |
0.658 |
ns |
| Experimental |
|
|
Rural |
pre |
pos |
466 |
-0.119 |
0.905 |
0.905 |
ns |
466 |
-0.119 |
0.905 |
0.905 |
ns |
| Experimental |
|
|
Urbana |
pre |
pos |
466 |
-1.645 |
0.101 |
0.101 |
ns |
466 |
-1.645 |
0.101 |
0.101 |
ns |
|
|
|
Rural |
Controle |
Experimental |
232 |
-1.069 |
0.286 |
0.286 |
ns |
232 |
-1.069 |
0.286 |
0.286 |
ns |
|
|
|
Urbana |
Controle |
Experimental |
232 |
-1.681 |
0.094 |
0.094 |
ns |
232 |
-1.681 |
0.094 |
0.094 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
122 |
3.462 |
0.051 |
3.426 |
0.046 |
3.423 |
0.045 |
3.335 |
3.512 |
122 |
3.462 |
0.051 |
3.426 |
0.046 |
3.423 |
0.045 |
3.335 |
3.512 |
0 |
| Experimental |
|
|
|
115 |
3.448 |
0.050 |
3.551 |
0.053 |
3.554 |
0.046 |
3.462 |
3.645 |
115 |
3.448 |
0.050 |
3.551 |
0.053 |
3.554 |
0.046 |
3.462 |
3.645 |
0 |
| Controle |
F |
|
|
60 |
3.481 |
0.070 |
3.533 |
0.063 |
3.524 |
0.063 |
3.399 |
3.649 |
60 |
3.481 |
0.070 |
3.533 |
0.063 |
3.524 |
0.063 |
3.399 |
3.649 |
0 |
| Controle |
M |
|
|
62 |
3.444 |
0.074 |
3.322 |
0.064 |
3.326 |
0.062 |
3.203 |
3.449 |
62 |
3.444 |
0.074 |
3.322 |
0.064 |
3.326 |
0.062 |
3.203 |
3.449 |
0 |
| Experimental |
F |
|
|
52 |
3.560 |
0.072 |
3.683 |
0.073 |
3.647 |
0.068 |
3.513 |
3.782 |
52 |
3.560 |
0.072 |
3.683 |
0.073 |
3.647 |
0.068 |
3.513 |
3.782 |
0 |
| Experimental |
M |
|
|
63 |
3.356 |
0.067 |
3.442 |
0.072 |
3.476 |
0.062 |
3.354 |
3.599 |
63 |
3.356 |
0.067 |
3.442 |
0.072 |
3.476 |
0.062 |
3.354 |
3.599 |
0 |
| Controle |
|
Rural |
|
43 |
3.532 |
0.080 |
3.364 |
0.080 |
3.331 |
0.076 |
3.180 |
3.482 |
43 |
3.532 |
0.080 |
3.364 |
0.080 |
3.331 |
0.076 |
3.180 |
3.482 |
0 |
| Controle |
|
Urbana |
|
51 |
3.365 |
0.088 |
3.443 |
0.073 |
3.465 |
0.070 |
3.326 |
3.603 |
51 |
3.365 |
0.088 |
3.443 |
0.073 |
3.465 |
0.070 |
3.326 |
3.603 |
0 |
| Experimental |
|
Rural |
|
47 |
3.441 |
0.079 |
3.456 |
0.083 |
3.453 |
0.073 |
3.309 |
3.597 |
47 |
3.441 |
0.079 |
3.456 |
0.083 |
3.453 |
0.073 |
3.309 |
3.597 |
0 |
| Experimental |
|
Urbana |
|
43 |
3.393 |
0.088 |
3.588 |
0.077 |
3.600 |
0.076 |
3.449 |
3.750 |
43 |
3.393 |
0.088 |
3.588 |
0.077 |
3.600 |
0.076 |
3.449 |
3.750 |
0 |
| Controle |
|
|
Rural |
41 |
3.518 |
0.084 |
3.422 |
0.065 |
3.400 |
0.078 |
3.246 |
3.554 |
41 |
3.518 |
0.084 |
3.422 |
0.065 |
3.400 |
0.078 |
3.246 |
3.554 |
0 |
| Controle |
|
|
Urbana |
81 |
3.434 |
0.064 |
3.427 |
0.061 |
3.435 |
0.056 |
3.326 |
3.545 |
81 |
3.434 |
0.064 |
3.427 |
0.061 |
3.435 |
0.056 |
3.326 |
3.545 |
0 |
| Experimental |
|
|
Rural |
35 |
3.537 |
0.090 |
3.552 |
0.105 |
3.523 |
0.085 |
3.356 |
3.689 |
35 |
3.537 |
0.090 |
3.552 |
0.105 |
3.523 |
0.085 |
3.356 |
3.689 |
0 |
| Experimental |
|
|
Urbana |
80 |
3.410 |
0.060 |
3.551 |
0.060 |
3.568 |
0.056 |
3.457 |
3.678 |
80 |
3.410 |
0.060 |
3.551 |
0.060 |
3.568 |
0.056 |
3.457 |
3.678 |
0 |